Methods and apparatus to count people in images

ABSTRACT

Methods and apparatus to count people in images are disclosed. An example method includes dividing a frame of image data into a plurality of segments; calculating first fluctuation factors for the respective segments; calculating a second fluctuation factor for the frame; and identifying ones of the segments having a first fluctuation factor greater than the second fluctuation factor as active segments.

RELATED APPLICATIONS

This patent is related to U.S. patent application Ser. No. 13/434,330,filed on Mar. 29, 2012, entitled “Methods and Apparatus to Count Peoplein Images.” This patent is related to U.S. patent application Ser. No.13/434,302, filed on Mar. 29, 2012, now U.S. Pat. No. 8,660,307,entitled “Methods and Apparatus to Count People in Images.” This patentis related to U.S. patent application Ser. No. 13/434,319, filed on Mar.29, 2012, now U.S. Pat. No. 8,761,442, entitled “Methods and Apparatusto Count People in Images.” Each of the related applications isincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally for audience measurement and, moreparticularly, to methods and apparatus to count people in images.

BACKGROUND

Audience measurement of media (e.g., content and/or advertisements, suchas broadcast television and/or radio programs and/or advertisements,stored audio and/or video programs and/or advertisements played backfrom a memory such as a digital video recorder or a digital video disc,audio and/or video programs and/or advertisements played via theInternet, video games, etc.) often involves collection of mediaidentifying data (e.g., signature(s), fingerprint(s), embedded code(s),channel information, time of presentation information, etc.) and peopledata (e.g., user identifiers, demographic data associated with audiencemembers, etc.). The media identifying data and the people data can becombined to generate, for example, media exposure data indicative ofamount(s) and/or type(s) of people that were exposed to specificpiece(s) of media.

In some audience measurement systems, the collected people data includesan amount of people in a media exposure environment (e.g., a televisionroom, a family room, a living room, a cafeteria at a place of businessor lounge, a television viewing section of a store, restaurant, a bar,etc.). To calculate the amount of people in the environment, somemeasurement systems capture image(s) of the environment and analyze theimage(s) to determine how many people appear in the image(s) at aparticular date and time. The calculated amount of people in theenvironment can be correlated with media being presented in theenvironment at the particular date and time to provide exposure data(e.g., ratings data) for that media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example media exposure environmentincluding an example audience measurement device disclosed herein.

FIG. 2 is an illustration of an example overlap region associated withthe first and second image sensors of FIG. 1.

FIG. 3 is an illustration of an example face rectangle detected by theexample audience measurement device of FIG. 1.

FIG. 4 is a block diagram of an example implementation of the exampleaudience measurement device of FIGS. 1 and/or 2.

FIG. 5 is a block diagram of an example implementation of the examplepeople counter of FIG. 4.

FIG. 6 is a block diagram of an example implementation of the exampleface detector of FIG. 5.

FIG. 7 is a block diagram of an example implementation of the examplestatic false positive eliminator of FIG. 5.

FIG. 8 is a block diagram of an example implementation of the exampleframe pair overlap eliminator of FIG. 5.

FIG. 9 is a block diagram of an example implementation of the examplegrouper of FIG. 5.

FIG. 10 is a block diagram of an example implementation of the examplegroup overlap eliminator of FIG. 5.

FIG. 11 is a block diagram of an example implementation of the exampleblob tally generator of FIG. 5.

FIG. 12 is a flowchart representative of example machine readableinstructions that may be executed to implement the example peoplecounter of FIGS. 4 and/or 5.

FIG. 13 is a flowchart representative of example machine readableinstructions that may be executed to implement the example face detectorof FIGS. 5 and/or 6.

FIGS. 14A and 14B are flowcharts representative of example machinereadable instructions that may be executed to implement the examplestatic false positive eliminator of FIGS. 5 and/or 7.

FIG. 15 is a flowchart representative of example machine readableinstructions that may be executed to implement the example static falseeliminator of FIGS. 5 and/or 7.

FIG. 16 is a flowchart representative of example machine readableinstructions that may be executed to implement the example frame pairoverlap eliminator of FIGS. 5 and/or 8.

FIG. 17 is a flowchart representative of example machine readableinstructions that may be executed to implement the example grouper ofFIGS. 5 and/or 9.

FIG. 18 is a flowchart representative of example machine readableinstructions that may be executed to implement the example group overlapeliminator of FIGS. 5 and/or 10.

FIG. 19 is a flowchart representative of example machine readableinstructions that may be executed to implement the example blob tallygenerator of FIGS. 5 and/or 11.

FIG. 20 is a block diagram of an example processing system capable ofexecuting the example machine readable instructions of FIGS. 12-19 toimplement the example people counter of FIGS. 4-11.

DETAILED DESCRIPTION

A plurality of applications, systems, and devices (e.g., surveillancesystems, consumer behavior monitors deployed in shopping centers,audience measurement devices, etc.) benefit from an ability toaccurately count a number of people in a particular space or environmentat a particular time. Such systems typically capture images of themonitored environment and analyze the images to determine how manypeople are present at certain times. While example methods, apparatus,and articles of manufacture disclosed herein to count people in imagesare described below in connection with a media exposure environment,examples disclosed herein can be employed in additional and/oralternative contexts, environments, applications, devices, systems, etc.that count people in images.

To count people in a media exposure environment, some audiencemeasurement systems attempt to recognize objects as humans in image datarepresentative of the media exposure environment. In traditionalsystems, the audience measurement system maintains a tally for eachframe of image data to reflect an amount of people in the environment ata time corresponding to a respective frame. Recognition of an object asa human in a frame of image data causes the traditional audiencemeasurement devices to increment the tally associated with that frame.However, faces of people often are undetected or unrecognized due to,for example, partial visibility, lighting conditions, obscuring of theface due to eating or drinking, or a rotation of a head relative to acamera capturing the frames, etc. Additionally, faces of people in themedia exposure environment may go undetected or unrecognized due tofield of view limitations associated with an image sensor. In otherwords, the image sensor tasked with captured images of the mediaexposure environment may not have a wide enough field of view to capturecertain faces of people that are being exposed to media. Additionally, anon-human object, such as a picture of a human face hanging on a wall,is sometimes mistaken for a human face, thereby improperly inflating thetally for the corresponding frame. An identification of a non-humanobject as a face is referred to herein as a false positive.

These and other limitations and/or inaccuracies can lead to aninaccurate tally of people for individual frames. An inaccurate tally ofpeople in a frame can negatively affect the accuracy of media exposuredata generated using the tally. For example, an audience measurementsystem counting the people in a room may also be collecting mediaidentifying information to identify media being presented (e.g., aurallyand/or visually) in the room. With the identification of the media andthe amount of people in the room at a given date and time, the audiencemeasurement system can indicate how many people were exposed to thespecific media and/or associate the demographics of the people todetermine audience characteristics for the specific media. When face(s)are not detected or recognized as faces, the exposure data for theidentified media may be undercut (e.g., the media is accredited withless viewers/listeners than had actually been exposed to the media).Alternatively, when false positives are detected, the exposure data forthe identified media may be overstated (e.g., the media is accreditedwith more viewers/listeners than had actually been exposed to themedia).

Example methods, apparatus, and articles of manufacture disclosed hereinincrease the accuracy of people tallies or counts by analyzing imagescaptured over a period of time. To compensate for limitations of facialdetection abilities on a frame-by-frame basis (e.g., a face that isproperly identified as a face in a first frame may not be identified asa face in a second frame despite the actual presence of the face in thesecond frame), examples disclosed herein analyze a plurality of framescaptured over a period of time (e.g., one minute) to generate a peopletally for that period of time. That is, rather than generating peopletallies for each individual frame, examples disclosed herein generate apeople tally for a time interval as a whole. However, analyzing imagestaken over a period of time as a whole creates challenges for accuratelycounting the people appearing in the images. For example, a person maymove during the period of time and, as a result, has the potential to becounted twice for the period of time. Example methods, apparatus, andarticles of manufacture disclosed herein address challenges presented byanalyzing images taken over a period of time as a whole. As described indetail below, examples disclosed herein group together face detectionsusing a grouping algorithm and eliminate transient detections thatshould not be included in the corresponding people tally.

Further, example methods, apparatus, articles of manufacture disclosedherein condition individual frames of a period of time before the facedetections are grouped together and counted for the period of time. Forexample, methods, apparatus, and articles of manufacture disclosedherein eliminate static false positives from the individual images usingone or more example techniques disclosed herein. As described in detailbelow, examples disclosed herein analyze fluctuation factors (e.g., rootmean square values) of the individual images to identify and eliminatestatic false positives. Additionally or alternatively, examplesdisclosed herein correlate face detections of a current frame tosuccessful face detections of previous frames to identify and eliminatestatic false positives. The elimination of false positives from theindividual frames increases the accuracy of facial identification forthe individual frames. In turn, the example grouping analysis disclosedherein is provided with conditioned, more accurate face detection data.Because the example grouping analysis disclosed herein is based on moreaccurate face detection data, the corresponding people tally for eachperiod of time is more accurate.

Further, example methods, apparatus, and articles of manufacturedisclosed herein increase accuracy of people counts or tallies generatedvia processing image data captured by multiple image sensors. Toincrease field of view capabilities and, thus, the likelihood that eachperson located in a monitored environment is detected and counted,examples disclosed herein utilize multiple images sensors tosimultaneously capture multiple images of the environment for aparticular time. However, the use of multiple image sensors createschallenges for accurately counting the people appearing in the images.For example, when the fields of view of the individual image sensorsintersect to form an overlap region, a person located in the overlapregion has the potential to be counted twice when detected by bothcameras. As described in detail below, examples disclosed hereindetermine whether an overlap detection has occurred in connection withindividual frames captured over a period of time and, if so, eliminatethe corresponding face detections for the individual frames.Additionally, when examples disclosed herein group together facedetections for the period of time using the individual frames, examplesdisclosed herein also eliminate redundant ones of the groups that fallin the overlap region. Thus, examples disclosed herein eliminateredundant face detections in individual frames collected over a periodof time, as well as redundant face detection groups formed from for theperiod of time using the individual frames. As described in detailbelow, the multiple eliminations of overlapping face detections providedby examples disclosed herein increase the accuracy of people talliesgenerated for periods of time using multiple image sensors.

Further, example methods, apparatus, and articles of manufacturedisclosed herein enable image sensors that provide image data to facedetection logic to capture frames at an increased rate by identifyingactive segments of frames of image data and limiting the face detectionlogic to the active segments. As described in detail below, examplesdisclosed herein divide frames into segments and analyze each segment todetermine whether one or more factors indicate a presence of a person.In some instances, examples disclosed herein compare a fluctuationfactor of each segment to an average fluctuation factor associated withthe frame. Because image data representative of human beings tends tofluctuate more than image data representative of static objects, thesegments having greater than average fluctuation factors are identifiedas active segments. Further, examples disclosed herein link adjacentactive segments to form regions of interest. In some examples, facedetection logic is executed only on the regions of interest, therebyreducing the computational load associated with the face detectionlogic. The reduced computational load enables an increased frame ratefor the image sensors. By increasing the frame rate, examples disclosedherein increase opportunities to detect faces, reduce false positives,and provide faster computational capabilities.

FIG. 1 is an illustration of an example media exposure environment 100including a media presentation device 102 and an example audiencemeasurement device 104 for measuring an audience 106 of the mediapresentation device 102. In the illustrated example of FIG. 1, the mediaexposure environment 100 is a room of a household that has beenstatistically selected to develop television ratings data forpopulation(s)/demographic(s) of interest. The example audiencemeasurement device 104 can be implemented in additional and/oralternative types of environments such as, for example, a room in anon-statistically selected household, a theater, a restaurant, a tavern,a retail location, an arena, etc. In the illustrated example of FIG. 1,the media presentation device is a television 102 coupled to a set-topbox (STB) 108 (e.g., a cable television tuning box, a satellite tuningbox, etc.). The STB 108 may implement a digital video recorder (DVR). Adigital versatile disc (DVD) player may additionally or alternatively bepresent. The example audience measurement device 104 can be implementedin connection with additional and/or alternative types of mediapresentation devices such as, for example, a radio, a computer, atablet, a cellular telephone, and/or any other communication device ableto present media to one or more individuals.

The example audience measurement device 104 of FIG. 1 utilizes first andsecond image sensors 110 and 112 to capture a plurality of frame pairsof image data of the environment 100. The first image sensor 110captures a first image within a first field of view and the second imagesensor 112 simultaneously (e.g., within a margin of error) captures asecond image within a second field of view. The first and second imagesare linked (e.g., by a tag or by common timestamp) to form a frame paircorresponding to a time at which the frames were captured. The fields ofview of the image sensors 110, 112 are shown in FIG. 1 with dottedlines. While shown in a cross-eyed arrangement in FIG. 1, the first andsecond image sensors 110 and 112 can be arranged in any suitable manner.Further, the example audience measurement device 104 can include morethan two image sensors. Further, the example audience measurement device104 can receive (e.g., via wired or wireless communication) data fromimage sensors located outside of the audience measurement device 104,such as an image sensor located along the wall on which the audiencemeasurement device 104 is implemented or elsewhere in the media exposureenvironment 100. In some examples, the audience measurement device 104is implemented by the Microsoft Kinect® sensor.

In the example shown in FIG. 1, the audience 106 includes three peopleand, thus, an accurate people tally for the environment 100 shown inFIG. 1 will equal three. As described in detail below, the exampleaudience measurement device 104 of FIG. 1 also monitors the environment100 to identify media being presented (e.g., displayed, played, etc.) bythe television 102 and/or other media presentation devices to which theaudience 106 is exposed. Identifying information associated with mediato which the audience 106 is exposed is correlated with the peopletallies to generate exposure data for the presented media. Therefore,the accuracy of the media exposure data depends on the ability of theaudience measurement device 104 to accurately identify the amount ofpeople in the audience 106 as three.

FIG. 2 shows intersections of the fields of view of the first and secondimage sensors 110 and 112 of the example audience measurement device 104of FIG. 1. Patterns of the example fields of view shown in FIG. 2 mayvary depending on, for example, capabilities (e.g., depth of captureranges) of the image sensors 110 and 112 and/or the arrangement of theimage sensors 110 and 112. A first region including the field of view ofthe first image sensor 110 is labeled with reference numeral 200 in FIG.2. A second region including the field of view of the second imagesensor 112 is labeled with reference numeral 202 in FIG. 2. An overlapregion in which the first region 200 and the second region 202 intersectis labeled with reference numeral 204 in FIG. 2. As described in detailbelow, a person detected in the overlap region 204 is susceptible tobeing double counted and, thus, causing incorrect people tallies. In theillustrated example of FIG. 1, a first person 114 falls in the firstregion 200, a second person 116 falls in the second region 202, and athird person 118 falls in the overlap region 204. Because the thirdperson 118 may be counted in connection with both the first image sensor110 and the second image sensor 112, the audience 106 has the potentialto be incorrectly identified as including four people. As describedbelow, examples disclosed herein enable the audience measurement device104 to identify the third person 118 as located in the overlap region204 and, thus, capable of being redundantly counted. Furthermore,examples disclosed herein enable the audience measurement device 104 todisqualify a redundant detection of the third person 118 from inclusionin a people tally to ensure the tally is accurate.

The example audience measurement device 104 detects faces in the regions200-204 and generates people tallies based on the face detections. Inthe illustrated example, the audience measurement device 104 detects anobject having characteristics of a human face and assigns a rectangle(or any other shape, such as a square, an oval, a circle, etc.) to thatobject. While the frame can be any suitable shape, the assigned frame isreferred to herein as a face rectangle. FIG. 3 illustrates an exampleface rectangle 300 assigned to a face of a person 302 detected in one ofthe regions 200-204 of FIG. 2. In the illustrated example, the audiencemeasurement device 104 generates the face rectangle around the detectedface to demarcate a position in the image data at which the face islocated. The example face rectangle 300 of FIG. 3 is centered on a point304 at a center of the detected face. The size of the example facerectangle 300 of FIG. 3 ranges between 32×32 pixels and 100×100 pixelsdepending on adjustable settings of the audience measurement device 104.The example audience measurement device 104 also records a position ofthe detected face. In the illustrated example of FIG. 3, the recordedposition 304 is defined by X-Y coordinates at a center of the face box300 (e.g., the point 304 of FIG. 3) surrounding the face. The X-Ycoordinates corresponds to a two-dimensional grid overlaid on the firstframe.

FIG. 4 is a block diagram of an example implementation of the exampleaudience measurement device 104 of FIG. 1. The example audiencemeasurement device 104 of FIG. 4 includes an audience detector 400 and amedia identifier 402. The example audience detector 400 of FIG. 4includes a first image sensor 404 and a second sensor 405 thatrespectively correspond to the first and second image sensors 110 and112 of FIGS. 1 and 2. The example audience detector 400 of FIG. 4 alsoincludes a people counter 406, a time stamper 408, and a memory 410. Theexample image sensors 404 and 405 of FIG. 2 capture frame pairs of imagedata of the environment 100, which includes the audience 106 beingexposed to a presentation output by the media presentation device 102 ofFIG. 1. In some examples, the image sensors 404 and 405 only captureframes of image data when the media presentation device 102 is in an“on” state and/or when the media identifier 402 determines that media isbeing presented in the environment 100 of FIG. 1. The image sensors 404and 405 may be implemented as any suitable device such as, for example,an infrared imager or a digital camera, such as a charge-coupled device(CCD) camera. In the illustrated example, the image sensors 404 and 405are implemented by cameras operating at a native resolution of1600×1200. In some instances, image data captured by the image sensors404 and 405 is reduced to 1200×900 for processing to reducecomputational load. To enable the image sensors 404 and 405 to operateunder a wide range of lighting conditions (e.g., ranging from sunlightto darkness), infrared (IR) light filter(s) are removed from the cameras404 and 405 such that IR light can be detected.

The frame pairs obtained by the image sensors 404 and 405 of FIG. 4 areconveyed to the people counter 406. In the illustrated example of FIG.4, the people counter 406 determines and records how many people arepresent in the media exposure environment 100 of FIG. 1 for a particularperiod of time (e.g., a minute) using the received frame pairs. Themanner in which the example people counter 406 of FIG. 4 performs itsoperations is described in detail below in connection with FIGS. 5-19.

The example people counter 406 of FIG. 4 outputs calculated peopletallies along with the corresponding frames to the time stamper 408. Thetime stamper 408 of the illustrated example includes a clock and acalendar. The example time stamper 408 associates a time period (e.g.,1:00 a.m. Central Standard Time (CST) to 1:01 a.m. CST) and date (e.g.,Jan. 1, 2012) with each calculated tally by, for example, appending theperiod of time and date information to an end of the tally data. In someexamples, the timestamper 408 applies a time and date, rather than atime of period. A data package (e.g., the tally, the timestamp, and theimage data) is stored in the memory 410. The memory 410 may include avolatile memory (e.g., Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM, etc.) and/or a non-volatile memory (e.g., flash memory). Thememory 410 may also include one or more mass storage devices such as,for example, hard drive disk(s), compact disk drive(s), digitalversatile disk drive(s), etc.

The example media identifier 402 of FIG. 4 includes a program detector412 and an output device 414. The example program detector 412 of FIG. 4detects presentation(s) of media in the media exposure environment 100and collects identification information associated with the detectedpresentation(s). For example, the program detector 412, which may be inwired and/or wireless communication with the presentation device 102and/or the STB 108 of FIG. 1, can identify a presentation time and asource (e.g., a tuned channel) of a presentation. The presentation timeand the source identification data may be utilized to identify theprogram by, for example, cross-referencing a program guide configured,for example, as a look up table. The source identification data may, forexample, be the identity of a channel obtained, for example, bymonitoring a tuner of the STB 108 or a digital selection (e.g., a remotecontrol signal) of a channel to be presented on the television 102.Additionally or alternatively, codes embedded with or otherwisebroadcast with media being presented via the STB 108 and/or thetelevision 102 may be utilized by the program detector 412 to identifythe presentation. As used herein, a code is an identifier that istransmitted with the media for the purpose of identifying and/or tuningthe corresponding media (e.g., an audience measurement code, a PIU usedfor tuning, etc.). Codes may be carried in the audio, in the video, inthe metadata, in the vertical blanking interval, or in any other portionof the media. Additionally or alternatively, the program detector 412can collect a signature representative of a portion of the media. Asused herein, a signature is a representation of some characteristic ofthe media (e.g., a frequency spectrum of an audio signal). Collectedsignature(s) can be compared against a collection of signatures of knownmedia to identify the corresponding media. The signature(s) can becollected by the program detector 412 and/or the program detector 412can collect samples of the media and export them to a remote site forgeneration of the signature(s). Irrespective of the manner in which themedia of the presentation is identified, the identification informationis time stamped by the time stamper 408 and stored in the memory 410.

In the illustrated example of FIG. 4, the output device 414 periodicallyand/or aperiodically exports the recorded data from the memory 414 to adata collection facility via a network (e.g., a local-area network, awide-area network, a metropolitan-area network, the Internet, a digitalsubscriber line (DSL) network, a cable network, a power line network, awireless communication network, a wireless mobile phone network, a Wi-Finetwork, etc.). The data collection facility utilizes the people datagenerated by the audience detector 400 and the media identifying datacollected by the media identifier 402 to generate exposure information.Alternatively, the data analysis could be performed locally and exportedvia a network or the like to a data collection facility for furtherprocessing. For example, the amount of people (as counted by the peoplecounter 406) in the exposure environment 100 for a period of time (asindicated by the time stamp appended to the people tally by the timestamper 408) in which a sporting event (as identified by the programdetector 412) was presented by the television 102 can be used in arating calculation for the sporting event. In some examples, additionalinformation (e.g., demographic data, geographic data, etc.) iscorrelated with the exposure information at the data collection facilityto expand the usefulness of the raw data collected by the exampleaudience measurement device 104 of FIGS. 1 and/or 4. The data collectionfacility of the illustrated example compiles data from many exposureenvironments.

While an example manner of implementing the audience measurement device104 of FIG. 1 has been illustrated in FIG. 4, one or more of theelements, processes and/or devices illustrated in FIG. 4 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example audience detector 400, theexample media identifier 402, the first example image sensor 404, thesecond example image sensor 405, the example people counter 406, theexample time stamper 408, the example program detector 412, the exampleoutput device 414, and/or, more generally, the example audiencemeasurement 104 of FIG. 4 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example audience detector 400, the examplemedia identifier 402, the first example image sensor 404, the secondexample image sensor 405, the example people counter 406, the exampletime stamper 408, the example program detector 412, the example outputdevice 414, and/or, more generally, the example audience measurement 104of FIG. 4 could be implemented by one or more circuit(s), programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)), etc. When any of the appended system or apparatusclaims of this patent are read to cover a purely software and/orfirmware implementation, at least one of the example audience detector400, the example media identifier 402, the first example image sensor404, the second example image sensor 405, the example people counter406, the example time stamper 408, the example program detector 412, theexample output device 414, and/or, more generally, the example audiencemeasurement 104 of FIG. 4 are hereby expressly defined to include atangible computer readable medium such as a memory, DVD, CD, Blu-ray,etc. storing the software and/or firmware. Further still, the exampleaudience measurement device 104 of FIG. 4 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 4, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

FIG. 5 is a block diagram of an example implementation of the examplepeople counter 406 of FIG. 4. The example people counter 406 of FIG. 5generates a tally representative of a number of people in the mediaexposure environment 100 of FIG. 1 for a period of time, such as oneminute. The period of time for which the example people counter 406generates a tally is adjustable via an interval tracker 500. The exampleinterval tracker 500 of FIG. 5 is incremented each time a frame pair isreceived at the people counter 406. Because the image sensors 404 and405 capture frame pairs at a certain rate (e.g., a frame pair every twoseconds), the number of frame pairs received at the people counter 406is translatable into a period of time (e.g., one minute). In theillustrated example, each time the example people counter 406 of FIG. 5receives a frame pair from the image sensors 404 and 405 of FIG. 4, avalue of the interval tracker 500 is incremented by one. When the valueof the example interval tracker 500 has reached a thresholdrepresentative of the period of time for which a people tally is to begenerated, the example interval tracker 500 triggers generation of apeople count for the frame pairs that were received during the period oftime. In the illustrated example, when the interval tracker 500 reachesa value of thirty, which corresponds to one minute of collecting framepairs, a people tally is generated as described below. After triggeringthe generation of the people count for a period time, the exampleinterval tracker 500 of FIG. 5 is reset to begin counting a number ofreceived frame pairs for the next period of time.

To identify people in the frame pairs received from the image sensors404 and 405, the example people counter 406 of FIG. 5 includes a facedetector 502. The example face detector 502 of FIG. 5 receives a firstframe of image data from the first image sensor 404 and a second frameof image data from the second image sensor 405. The first and secondframes of image data make up the frame pair corresponding to a firsttime (e.g., the first time interval, such as two seconds, of the periodof time for which a people tally is generated). An exampleimplementation of the face detector 502 is shown in FIG. 6. The exampleface detector 502 of FIG. 6 assigns an identifier (e.g., a tag based ona time at which the frame pair was captured) to the frame pair such thatthe frame pair can be identified when, for example, stored in a databaseand referenced by other components of the people counter 406.

The example face detector 502 includes an active region identifier 600and face detection logic 602. The example active region identifier 600analyzes image data of each frame of the frame pair to identify regionsof interest. The regions of interest identified by the example activeregion identifier 600 include areas of the frame that are determined tolikely include a human being rather than only static objects (e.g.,walls, furniture, floors, etc.). To do so, the example active regionidentifier 600 of FIG. 6 includes a frame segmenter 604 that divides theframe into component parts such as, for example, 50×50 pixel rectangles.An example fluctuation calculator 606 analyzes characteristics (e.g.,pixel intensities) of the segmented image data over a period of time tocalculate a fluctuation factor for each of the segments. The exampleactive region identifier 600 includes a fluctuation database 608 totrack the continuously calculated fluctuation factors for the segments.Thus, for each of the segments, the example fluctuation calculator 606calculates how much the respective image data fluctuated from one frameto the next. In the illustrated example of FIG. 6, the fluctuationcalculator 606 calculates a root mean square (RMS) value for each pixelof a segment and averages the calculated RMS values for the segment todetermine a collective RMS value for the segment. The collective RMSvalue is stored in the fluctuation database 608.

The example active region identifier 600 of FIG. 6 also includes athreshold calculator 610 to calculate an average fluctuation factor forthe current frame. In the illustrated example, the threshold calculator610 selects a random set of pixels (e.g., one thousand pixels) in thecurrent frame and calculates an average RMS value for the random set ofpixels. In the example of FIG. 6, the average RMS value for the randomset of pixels is the threshold. Therefore, the threshold for the exampleactive region identifier 600 varies over different frames. The exampleactive region identifier 600 includes a comparator 612 to compare theRMS value of each segment to the threshold. The example comparator 612designates segments having an RMS value greater than the threshold asactive segments. Thus, the example active region identifier 600identifies one or more segments within a frame that are active (e.g.,likely to include a human).

The example active region identifier 600 of FIG. 6 includes a segmentlinker 614 to link active segments together to form regions of interest.In the illustrated example, when any of the active segments identifiedby the comparator 612 overlap or are adjacent to each other (e.g.,within a margin of error), the example segment linker 614 joins thesegments to form a rectangle (or any other shape) representative of aregion of interest. Thus, the example active region identifier 600generates one or more regions of interest that span each active segmentlikely to include a human.

In the illustration example of FIG. 6, the face detection logic 602 isexecuted on the regions of interest identified by the active regionidentifier 600. Further, in the illustrated example, the face detectionlogic 602 is not executed on areas of the frame outside of the regionsof interest. By limiting the face detection logic 602 to the regions ofinterest, the example face detector 502 is able to, for example,increase a frame rate at which frames are captured. For example, becauseof the reduced computational load of having to analyze only a portion ofeach frame, the image sensors 404 and 405 of FIG. 4 can be set tocapture one frame every one second instead of every two seconds. Afaster capture rate leads to an increased amount of frames and, thus, anincreased sample size. As a result, the likelihood of properly detectingfaces over a period of time is increased.

The example face detection logic 602 of FIG. 6 analyzes the objects ofthe frame pair to determine whether one or more of the objects are facesvia, for example, face detection techniques and/or algorithms. In theillustrated example of FIG. 6, the face detection logic is unconcernedwith an identity of a person belonging to detected faces. However, insome examples, the face detection logic 602 may attempt to identify theperson by, for example, comparing image data corresponding to a detectedface to a collection of images known to belong to identifiable people(e.g., frequent visitors and/or members of the household associated withthe room 100 of FIG. 1).

When a first face is detected in the image data, the example facedetection logic 602 of FIG. 6 generates a face indication, such as arectangle surrounding a detected face as shown in FIG. 3, to mark aposition (e.g., X-Y coordinates) in the corresponding frame at which theface is located. For each detected face, the example face detectionlogic 602 of FIG. 6 passes information to a face data tracker 504 ofFIG. 5. In the illustrated example of FIGS. 5 and 6, the informationpassed to the face data tracker 504 for each frame includes anygenerated face rectangles and corresponding coordinate(s) within theframe, the image data outlined by the face rectangle, an indication ofwhich one of the image sensors 404 or 405 captured the frame, anindicator (e.g., the identifier assigned by the face detector 502) ofthe frame pair to which the face rectangle(s) belong, and a time stamp.The example face data tracker 504 of FIG. 5 includes a first set of facerectangles 506 detected in connection with the first image sensor 404and a second set of face rectangles 508 detected in connection with thesecond image sensor 405. The first and second sets of face rectangles506 and 508 store the received information related to the facerectangles detected by the face detector 502. In the illustratedexample, the face detector 502 also forwards the frame pair and thecorresponding image data to a frame database 510, which stores the framepair in a searchable manner (e.g., by time stamp and/or frame pairidentifier).

The example people counter 406 of FIG. 5 includes a static falsepositive eliminator 512 to remove face rectangles from the facerectangle sets 506 and 508 that correspond to false positives. Theexample static false positive eliminator 512 of FIG. 5 identifies whichof the face rectangles stored in the face rectangle sets 506 and 508 arefalse positives for the current frame pair. Upon finding a falsepositive, the example static false positive eliminator 512 of FIG. 5removes the corresponding face rectangle from the example face datatracker 504. As a result, the frame pairs stored in the face datatracker 504 include a more accurate representation of the audience 106in the media exposure environment 100 of FIG. 1. As a result, peoplecounts generated using the information of the example face data tracker504 are more accurate.

FIG. 7 illustrates an example implementation of the static falsepositive eliminator 512 of FIG. 5. The example static false positiveeliminator 512 of FIG. 7 includes a fluctuation-based eliminator 700 anda correlation-based eliminator 702. In the illustrated example of FIG.7, both the fluctuation-based eliminator 700 and the correlation-basedeliminator 702 are used to eliminate false positives from the facerectangle sets 506 and 508 of FIG. 5. In some examples, only one of thefluctuation-based eliminator 700 and the correlation-based eliminator702 is used to eliminate false positives from the frame pair.

The example fluctuation-based eliminator 700 of FIG. 7 includes a falsepositive identifier 704 and a checker 706. In the illustrated example ofFIG. 7, the false positive identifier 704 analyzes image data toidentify one or more potential false positives and the checker 706determines whether the potential false positive(s) correspond topreviously identified face(s) and, thus, are not false positives. Inother words, the example checker 706 determines whether any of thepotential false positives identified by the example false positiveidentifier 704 are actually true positives based on data associated withpreviously captured image data. However, in some examples, the falsepositive identifier 704 may operate without the checker 706 verifyingresults of operations performed by the false positive identifier 704.

The example false positive identifier 704 of FIG. 7 takes advantage offluctuations typically seen in image data associated with a human.Static objects (e.g., items that may be falsely identified as humanfaces, such as a picture of a human face) exhibit much lower pixelfluctuation than a live human face. To take advantage of thisdifference, the example false positive identifier 704 compares pixelintensity fluctuation of the face rectangles detected by the facedetector 502 of FIG. 5 with other portions of the image data (most ofwhich likely corresponds to static objects, such as furniture, floors,walls, etc.). In other words, the example false positive identifier 704of FIG. 7 determines whether the face rectangle(s) detected by the facedetector 502 of FIG. 5 include image data that fluctuates (e.g., inpixel intensity) more or less than image data corresponding to staticobjects.

The example false positive identifier 704 of FIG. 7 includes a pixelintensity calculator 708 that calculates an intensity value of eachpixel of a current frame of image data (e.g., the first frame of thecurrent frame pair captured by the first image sensor 404 or the secondframe of the current frame pair captured by the second image sensor405). The example false positive identifier 704 includes a fluctuationcalculator 710 that incorporates the calculated pixel intensity for eachpixel into a running fluctuation value associated with each pixel thatis stored in a fluctuation database 712. That is, the fluctuationdatabase 712 tracks a fluctuation value, as calculated by thefluctuation calculator 710, of each pixel captured by the correspondingimage sensor. In the illustrated example of FIG. 7, the fluctuationvalues calculated by the fluctuation calculator 710 and stored in thefluctuation database 712 are root mean square (RMS) values. However,additional or alternative types of fluctuation values can be utilized bythe example false positive identifier 704. The RMS values stored in thefluctuation database 712 represent the fluctuation of intensity of eachpixel over a period of time, such as the previous five minutes.

Thus, each pixel of a current frame (or frame pair) has an RMS valuestored in the fluctuation database 712. To utilize this information, theexample false positive identifier 704 of FIG. 7 includes an averager 714and a comparator 716. In the example of FIG. 7, the averager 714calculates an average RMS value of a random set of pixels for thecurrent frame. The example averager 714 generates the random set ofpixels using a random number generator. Because most of the pixels inthe image data are likely representative of static objects that do nothave large intensity fluctuations, the average RMS value of the randomset of pixels mainly represents the fluctuation of static objects (e.g.,which may occur due to, for example, lighting changes, shadows, etc.).The example averager 714 of FIG. 7 also calculates an average RMS valuefor the pixels of each detected face rectangle for the current frame(e.g., from one of the face rectangle sets 506 or 508). The average RMSvalue of each face rectangle represents the intensity fluctuation acrossthat face rectangle, which may include a human face (e.g., according tothe face detector 502 of FIG. 5). For each face rectangle detected inthe current frame, the example comparator 716 compares the average RMSvalue of the random set of pixels across the current frame to theaverage RMS of the respective face rectangle. If the respective facerectangle has an average RMS value less than or equal to the average RMSvalue of the random set of pixels, that face rectangle is determined tobe a false positive (e.g., because the face rectangle fluctuates similarto static objects). If the respective face rectangle has an average RMSvalue greater than the average RMS value of the random set of pixels,that face rectangle is determined to be a true positive.

The example checker 706 of FIG. 7 receives the face rectangles that havebeen designated by the false positive identifier 704 as false positivesand determines whether any of those face rectangles have previously(e.g., in connection with a previous frame or set of frames) beenverified as corresponding to a human face. The example checker 706 ofFIG. 7 includes a location calculator 718 to determine coordinates atwhich a received false positive face rectangle is located. In theillustrated example, the location calculator 718 retrieves the locationdata from the face data tracker 504 which, as described above, receiveslocation data in association with the detected face rectangles from theface detector 502. The example checker 706 of FIG. 7 also includes aprior frame retriever 720 that retrieves data from the frame database510 of FIG. 5 using the coordinates of the received false positive facerectangle. The example frame database 510 of FIG. 5 includes historicaldata indicative of successful face detections and the locations (e.g.,coordinates) of the successful face detections. The example prior frameretriever 720 queries the frame database 510 with the coordinates of thereceived false positive face rectangle to determine whether a face wasdetected at that location in a previous frame within a threshold amountof time (e.g., within the previous twelve frames). If the queryperformed by the prior frame retriever 720 does not return a frame inwhich a face was successfully detected at the location in the previousframe(s), a false positive verifier 722 of the example checker 706verifies that the received face rectangle is a false positive. If thequery performed by the prior frame retriever 720 returns a frame inwhich a face was successfully detected at the location in the previousframe(s), the false positive verifier 722 designates the received facerectangle as a true positive. That is, if the checker 706 determinesthat a face was recently detected at the location of a received falsepositive, the example checker 706 of FIG. 7 disqualifies that facerectangle as a false positive and, instead, designates the facerectangle as a true positive. The corresponding face rectangle set 506or 508 of the example face data tracker 504 of FIG. 5 is updatedaccordingly.

To provide an alternative or supplemental elimination of falsepositives, the example static false positive eliminator 512 of FIG. 7includes the correlation-based eliminator 702. The examplecorrelation-based eliminator 702 uses a comparison between a currentframe and historical frame data to determine whether one or more of theface rectangles of the sets of face rectangles 506 and/or 508 are staticfalse positives (e.g., include a static object rather than a humanface). Similar to the example checker 706 described above, the examplecorrelation-based eliminator 702 of FIG. 7 includes a locationcalculator 724 and a prior frame retriever 726. The example locationcalculator 724 of the correlation-based eliminator 702 determinescoordinates of a detected face rectangle by, for example, accessing thelocation information stored in connection with the current frame in theface data tracker 504 of FIG. 5. The example prior frame retriever 726of the correlation-based eliminator 702 retrieves a previous frame fromthe frame database 510 of FIG. 5. In the illustrated example, theexample prior frame retriever 726 retrieves a frame that occurredsixty-three frames prior to the current frame from the frame database510, which corresponds to approximately one hundred twenty-eightseconds. However, the prior frame retriever 726 can use alternativeseparation between the current frame and the previous frame. The examplecorrelation-based eliminator 702 also includes a data extractor 728 toextract image data from the current frame and the retrieved previousframe. The example data extractor 728 of FIG. 7 extracts image data fromthe current frame and the retrieved previous frame at the coordinatesprovided by the location calculator 724 corresponding to the facerectangle.

The example correlation-based eliminator 702 includes a score generator730 to generate a correlation score representative of a similaritybetween the image data extracted from the received face rectangle andthe image data extracted from the previous frame at the calculatedlocation of the received face rectangle. The score generator 730 usesone or more algorithms and/or techniques to generate the correlationscore accordingly to any suitable aspect of the image data such as, forexample, pixel intensity. Thus, the example score generator 730determines how similar the image data of the face rectangle is to imagedata of a previous frame at the same location as the face rectangle.Using the correlation score, the example correlation-based eliminator702 determines whether the image data of the face rectangle has changedover time in a manner expectant of a human face. To make thisdetermination, the example correlation-based eliminator 702 includes acomparator 732 to compare the correlation score generated by the examplescore generator 730 to a threshold, such as ninety-two percent. Whilethe threshold used by the example comparator 732 of FIG. 7 is ninety-twopercent, another threshold may likewise be appropriate. If thecorrelation score for the face rectangle meets or exceeds the threshold,the example comparator 732 determines that the face rectangle likelycorresponds to a static object and, thus, is a false positive. If hecorrelation score for the face rectangle is less than the threshold, theexample comparator 732 verifies that the face rectangle includes a humanface. The corresponding face rectangle set 506 or 508 of the exampleface data tracker 504 of FIG. 5 is updated accordingly.

Referring to FIG. 5, the example people counter 406 includes a framepair overlap eliminator 514 to avoid double counting of human faces inthe overlap region 204 (FIG. 2) associated with the first and secondimage sensors 404 and 405. The example frame pair overlap eliminator 514eliminates face rectangles detected in connection with the second imagesensor 405 that may have already been counted as detected in connectionwith the first image sensor 404.

An example implementation of the example frame pair overlap eliminator514 is illustrated in FIG. 8. The example frame pair overlap eliminator514 of FIG. 8 includes an overlap region analyzer 800 to analyze acurrent frame pair to determine whether any of the face rectanglesdetected by the face detector 502 in connection with the second imagesensor 405 fall in the overlap region 204 of FIG. 2. For example, theoverlap region analyzer 800 obtains coordinates for each face rectangledetected in connection with the second image sensor 405 and determineswhether the coordinates fall within borders of the overlap region 204.To obtain image data from the face rectangles associated with the secondimage sensor 405 that fall in the overlap region according to theoverlap region analyzer 800, the example frame pair overlap eliminator514 of FIG. 8 includes a data extractor 802. The example data extractor802 extracts any suitable data, such as pixel intensity or average pixelintensity, that can be used to compare the detected overlap facerectangle associated with the second image sensor 405 to other imagedata (e.g., image data associated with the first image sensor 404).

The example data extractor 802 also extracts image data from each of theface rectangles detected in connection with the first image sensor 404.The example frame pair overlap eliminator 514 of FIG. 8 includes a scoregenerator 804 to compare image data of a face rectangle identified bythe face detector 502 in connection with the second image sensor 405 andidentified by the overlap region analyzer 800 as falling in the overlapregion 204 with each face rectangle identified by the face detector 502in connection with the first image sensor 404. In other words, theexample score generator 804 compares face rectangles detected in theoverlap region 204 in connection with the second image sensor 405 toevery face rectangle detected in connection with the first image sensor405. The example score generator 804 of FIG. 8 generates a correlationscore for each of these comparisons. Any factor(s) can be used for thecomparisons such as, for example, pixel intensity, contrast, etc. Acomparator 806 of the example frame pair overlap eliminator 514 comparesthe correlation score(s) to a threshold (e.g., ninety-two percent) todetermine if the overlap face rectangle as sufficiently similar to oneof the face rectangles detected in connection with the first imagesensor 404. If the comparator 806 determines that the overlap facerectangle detected in connection with the second image sensor 405 issufficiently similar to one of the face rectangles detected inconnection with the first image sensor 404 (e.g., within the threshold),the comparator 806 designates the overlap rectangle detected inconnection with the second image sensor 405 as a redundant facerectangle and eliminates the face rectangle from the second set of facerectangles 508 of the data tracker 504 of FIG. 5.

Further, the example frame pair overlap eliminator 514 maintains acorrelated face history 808 (e.g., in a database) including facecorrelations identified by the comparator 806. That is, when a facerectangle detected in connection with the second image sensor 405 isdetermined to be redundant to a face rectangle detected in connectionwith the first image sensor 404, an entry is added to the correlatedface history 808. As described below, the example correlated facehistory 808 enables an identification of a redundant face rectanglewithout having to extract and/or analyze image data of a face rectangle.Instead, the example correlated face history 808 provides the ability toidentify a face rectangle detected in connection with the second imagesensor 405 as redundant based solely on coordinates of the detected facerectangles of the corresponding frame.

Because the field of view of the first image sensor 404 is differentfrom the field of view of the second image sensor 405, the coordinatesof the redundant face rectangle detected in connection with the secondimage sensor 405 are different from the coordinates of the correspondingface rectangle detected in connection with the first image sensor 405.In other words, even though the face rectangles are determined tocorrespond to the same face (e.g., by the comparator 806), therespective coordinates for each image sensor are different from theother. To determine a location of each face rectangle, the example framepair overlap eliminator 514 includes a location calculator 810. Theexample location calculator 810 of FIG. 8 determines first coordinatesfor the face rectangle detected in connection with the first imagesensor 404 and second coordinates for the overlap face rectangledetected in connection with the second image sensor 405 (the facerectangle determined to be redundant). The first and second coordinatesare linked together and stored in the correlated face history 808 ascorresponding to a redundant pair of face rectangles. Thus, thecorrelated face history 808 includes a plurality of entries, eachcorresponding to a pair of face rectangles determined to be redundant.Further, each entry of the correlated face history 808 includes firstcoordinates corresponding to the first image sensor 404 and secondcoordinates corresponding to the second image sensor 405. The firstcoordinates are referred to herein as first-camera coordinates and thesecond coordinates are referred to herein as second-camera coordinates.

The example frame pair overlap eliminator 514 includes a searcher 812 toquery the correlated face history 808 such that a face rectangledetected in connection with the second image sensor 405 in a currentframe pair can be identified as a redundancy based on its location andthe presence of another face rectangle detected in connection with thefirst image sensor 404 at the counterpart location of the first frame.As described above, the example overlap region analyzer 800 determinescoordinates of a face rectangle detected in connection with the secondimage sensor 405 in the overlap region for the current frame pair. Theexample searcher 812 uses the coordinates to query the correlated facehistory 808. The query is meant to determine if the correlated facehistory 808 includes an entry having second-camera coordinates matchingthe coordinates of the overlap face rectangle obtained by the overlapregion analyzer 800. If so, that entry is analyzed by an analyzer 814 toobtain the first-camera coordinates linked to the found second-cameracoordinates. The example analyzer 814 also analyzes the current framepair to determine whether the first image sensor 404 detected a facerectangle at the first-camera coordinates obtained from the correlatedface history 808. If so, the face rectangle detected in connection withthe second image sensor 405 in the current frame is determined by theanalyzer 814 to be a redundant face rectangle. That is, the analyzer 814determines that the two face rectangles of the current frame pair havethe same locations as a pair of face rectangles previously determined tobe redundant and, thus, are also redundant. Face rectangles determinedto be redundant by the example comparator 806 or the example analyzer814 are eliminated from the second set of face rectangles 508 so thatthose face rectangles are not double counted in later analyses.

Referring to FIG. 5, the example static false positive eliminator 512 ofFIGS. 5 and/or 7 and the example frame pair overlap eliminator 514 ofFIGS. 5 and/or 8 eliminate redundant and/or overlapping face rectangles,respectively, from the first and/or second sets of face rectangles 506,508. The example static false positive eliminator 512 and the exampleframe pair overlap eliminator 514 of FIG. 5 perform their operations oneach frame pair as the frame pair is received at the example peoplecounter 406 of FIG. 5.

As described above, the example interval tracker 500 determines when athreshold amount of frame pairs have been received at the people counter406 to trigger a calculation of a people tally for a correspondingperiod of time. For example, the interval tracker 500 may determine thatthirty frame pairs have been received at the people counter 406 andprocessed by the static false positive eliminator 512 and/or the framepair overlap eliminator 514. In response, the example interval tracker500 triggers a grouper 516 to initiate a calculation of a people tallyfor the thirty frame pairs. As part of the triggering, the exampleinterval tracker 500 provides the conditioned sets of rectangles 506 and508 to the grouper 516 and resets or clears the face rectangle sets 506and 508 so that data can be stored in the sets 506 and 508 for thesucceeding time interval. The information of the sets 506 and 508 may bestored or backed up in the frame database 510 before being reset. Insome examples, running averages are used, so a reset is not employed.

An example implementation of the example grouper 516 is illustrated inFIG. 9. The example grouper 516 of FIG. 9 includes a location calculator900 to obtain the location of the face rectangles of the sets 506 and508. As described above, location information of the face rectangles(e.g., coordinates of a center of the corresponding detected facerectangle as shown in FIG. 3) is stored in the example face data tracker504 and, thus, the example location calculator 900 retrieves thelocation information from the data tracker 504 in the illustratedexample. The example grouper 516 of FIG. 9 also includes a comparator902 to compare the retrieved locations of the face rectangles. Theexample comparator 902 determines whether any of the face rectangles ofthe sets 506, 508 collected over the period of time defined by theinterval tracker 500 are similarly located within a threshold. In theillustrated example, the threshold is actually two thresholds, namely avertical threshold or range corresponding to a Y-coordinate of thelocation information and a horizontal threshold or range correspondingto an X-coordinate of the location information. If two or more facerectangles of the sets 506 and 508 have locations corresponding to thevertical and horizontal thresholds, a combiner 904 of the grouper 516groups the similarly located face rectangles together to form a group.

After the face rectangles 506, 508 received by the grouper 516 inconnection with the current time interval are analyzed, the examplecombiner 904 of FIG. 9 determines whether any of the formed groups haveless than a threshold amount of member face rectangles. For example, thecombiner 904 may determine whether any of the formed groups have lessthan five face rectangles. If a group has less than the threshold amountof members, the example combiner 904 disqualifies that group as notincluding enough face detections to be reliable. A group having lessthan the threshold amount of members over the period of time defined bythe interval tracker 500 likely corresponds to transient detections offaces and/or includes false positives that survived the conditioningprovided by the example static false positive eliminator 512 of FIG. 5.In the illustrated example, for each group having the requisite amount(e.g., more than the threshold) of members, the combiner 904 generates alist of face rectangles belonging to the group and/or assigns anidentifier to face rectangles belonging to the group.

Referring to FIG. 5, the example people counter 406 includes a groupoverlap eliminator 518 to eliminate redundant groups. While the framepair overlap eliminator 514 attempts to eliminate all redundant facerectangles from individual frame pairs, redundant face rectangles maysurvive the conditioning provided by the frame pair overlap eliminator514. For example, redundant face rectangles not occurring in the sameframe pair are not eliminated by the example frame pair overlapeliminator 514. Accordingly, the example group overlap eliminator 518analyzes the groups provided by the grouper 516 for redundant groups.

An example implementation of the group overlap eliminator 518 isillustrated in FIG. 10. The example group overlap eliminator 518 of FIG.10 includes an overlap region analyzer 1000 to determine whether any ofthe groups provided by the grouper 516 corresponding to the second imagesensor 405 are located in the overlap region 204. In the illustratedexample, the overlap region analyzer 1000 obtains an average center ofthe face rectangles of each group and determines whether the averagecenter falls within borders of the overlap region 204. To obtain imagedata from the face rectangles of any group(s) associated with the secondimage sensor 405 and falling in the overlap region 204, the examplegroup overlap eliminator 518 of FIG. 10 includes a data extractor 1002.The example data extractor 1002 extracts any suitable data, such aspixel intensity or average pixel intensity, to be used to compare theface rectangles of the overlap group to other image data. The exampledata extractor 1002 also extracts image data from each of the facerectangles detected in connection with the first image sensor 404.

The example frame pair overlap eliminator 518 of FIG. 10 includes ascore generator 1004 to compare image data of the overlap groupassociated with the second image sensor with each face rectangleidentified by the face detector 502 in connection with the first imagesensor 404. In other words, a face rectangle of a group detected in theoverlap region 204 in connection with the second image sensor 405 iscompared to every face rectangle detected in connection with the firstimage sensor 405. The example score generator 1004 of FIG. 10 generatesa correlation score for each of these comparisons. A comparator 1006 ofthe example group overlap eliminator 518 compares the correlationscore(s) to a threshold (e.g., ninety-two percent) to qualify theoverlap group face rectangle as sufficiently similar to one of the facerectangles detected in connection with the first image sensor 404. Ifthe comparator 1006 determines that the group overlap face rectangledetected in connection with the second image sensor 405 is sufficientlysimilar to one of the face rectangles detected in connection with thefirst image sensor 404, the comparator 1006 designates the overlap groupdetected in connection with the second image sensor 405 as redundant.

Referring to FIG. 5, the group overlap eliminator 518 provides thesurviving groups to a group tally generator 520. In the illustratedexample, the group tally generator 520 counts the number of groupsprovided by the group overlap eliminator 518 to form a people tally forthe period of time defined by the interval tracker 500. The calculatedpeople tally is provided to a discrepancy resolver 522, which isdescribed in detail below.

The example people counter 406 also includes a blob tally generator 524that also generates a people tally for the period of time defined by theinterval tracker 500. The example blob tally generator 524 creates oneor more blobs based on the sets of face rectangles 506 and 508 and countthe blobs to develop a people tally for the period of time. As describedbelow in connection with the discrepancy resolver 522, the people tallygenerated by the blob tally generator 524 can be used to verify orsubstitute for the people tally provided by the group tally generator520 of FIG. 5.

An example implementation of the blob tally generator 524 is illustratedin FIG. 11. The example blob tally generator 524 of FIG. 11 includes apixel whitener 1100 to whiten each pixel of the face rectangles 506 and508. Additionally, the example pixel whitener 1100 of FIG. 11 blackenseach pixel not corresponding to a detected face. The blob tallygenerator 524 also includes a blob creator 1102 to use thewhitened/blackened pixel data to form a blob image. In particular, theblob creator 1102 combines or overlays the whitened/blackened image dataspanning across the period of time defined by the interval tracker 500.The whitened pixels of the combined image data is likely to correspondto a human face. That is, each blob of the blob image is counted byincrementing the people tally.

However, because the example people counter 406 of FIG. 5 counts peopleusing multiple image sensors, some blobs provided by the blob creator1102 may be double counted. To eliminate redundant blobs associated withthe second image sensor 405, the example blob tally generator 524includes a center of gravity calculator 1104 to identify a center ofblobs provided by the blob creator 1102 connected to the second set offace rectangles 508 and, thus, the second image sensor 405. Inparticular, the example center of gravity calculator 1104 calculates amedian vertical position and a median horizontal position of a blob.Other techniques of calculating the center of gravity of a blob arepossible. The example blob tally generator 524 of FIG. 11 includes alocation analyzer 1106 to determine whether the calculated center ofgravity for a blob associated with the second image sensor 405 fallswithin boundaries of the overlap region 204. If the center of gravity ofthe blob falls within the boundaries of the overlap region 204, the blobis eliminated from the blob image formed by the blob creator 1102. Anadder 1108 of the example blob tally generator 524 counts the survivingblobs to form a blob people tally.

In the illustrated example of FIG. 5, the generated blob people tally isprovided to the discrepancy resolver 522. The example discrepancyresolver 522 also receives a group people tally from the group tallygenerator 520. If the discrepancy resolver 522 compares the two receivedtallies and determines that the tallies are the same, the discrepancyresolver 522 stores the people tally as a found people tally in a tallydatabase 526 in connection with an identifier for the correspondingperiod of time, the length of which is defined by the interval tracker500. Additionally, when there is no difference between the two talliesprovided to the discrepancy resolver 522, the corresponding data storedin tally database 526 also includes an indication that both the grouppeople tally and the blob people tally included the same number for thatperiod of time. Therefore, the example tally database 526 of FIG. 5includes a plurality of people tallies, each representative of a numberof people in the media exposure environment 100 of FIG. 1 correspondingto a period of time.

When the people tally generated by the group tally generator 520 for afirst period of time is different from the blob people tally generatedby the blob tally generator 524 for the same first period of time, theexample discrepancy resolver 522 of FIG. 5 analyzes entries of the tallydatabase 526 corresponding to other periods of time near the firstperiod of time. In particular, the example discrepancy resolver 522 ofFIG. 5 determines the people tally stored in the tally database 526 fora certain amount of preceding periods of time. For example, when thefirst period of time is a minute beginning at 1:30 a.m., the discrepancyresolver 522 obtains the people tallies stored in the tally database 526for 1:29 a.m., 1:28 a.m., and 1:27 a.m. The example discrepancy resolver522 of FIG. 5 uses the data associated with preceding periods of time tochoose one of the differing first and second tallies as the found peopletally for the first period of time. In the illustrated example, thediscrepancy resolver 522 chooses whichever of the differing first andsecond tallies matches an average tally of the preceding tallies. If nosuch match exists, the example discrepancy resolver 522 selectswhichever of the differing first and second tallies is closest to theaverage tally of the preceding tallies. The example discrepancy resolver522 of FIG. 5 can utilize the data of the tally database 526 for thepreceding periods of time in additional or alternative manners toresolve the discrepancy between the group tally generator 520 and theblob tally generator 524.

While an example manner of implementing the people counter 406 of FIG. 2has been illustrated in FIGS. 5-11, one or more of the elements,processes and/or devices illustrated in FIGS. 5-11 may be combined,divided, re-arranged, omitted, eliminated and/or implemented in anyother way. Further, the example interval tracker 500, the example facedetector 502, the example face data tracker 504, the example staticfalse positive eliminator 512, the example frame pair overlap eliminator514, the example grouper 516, the example group overlap eliminator 518,the example group tally generator 520, the example discrepancy resolver522, the example blob tally generator 524, the example active regionidentifier 600, the example frame segmenter 604, the example fluctuationcalculator 606, the example threshold calculator 610, the examplecomparator 612, the example segment linker 614, the example facedetection logic 602, the example fluctuation-based eliminator 700, theexample correlation-based eliminator 702, the example false positiveidentifier 704, the example checker 706, the example pixel intensitycalculator 708, the example fluctuation calculator 710, the exampleaverager 714, the example comparator 716, the example locationcalculator 718, the example prior frame retriever 720, the example falsepositive verifier 722, the example location calculator 724, the exampleprior frame retriever 726, the example data extractor 728, the examplescore generator 730, the example comparator 732, the example overlapregion analyzer 800, the example data extractor 802, the example scoregenerator 804, the example comparator 806, the example correlated facehistory 808, the example location calculator 810, the example searcher812, the example analyzer 814, the example location calculator 900, theexample comparator 902, the example combiner 904, the example overlapregion analyzer 1000, the example data extractor 1002, the example scoregenerator 1004, the example comparator 1006, the example pixel whitener1100, the example blob creator 1102, the example center of gravitycalculator 1104, the example location analyzer 1106, the example adder1108, and/or, more generally, the example people counter 406 of FIGS.5-11 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example interval tracker 500, the example face detector 502,the example face data tracker 504, the example static false positiveeliminator 512, the example frame pair overlap eliminator 514, theexample grouper 516, the example group overlap eliminator 518, theexample group tally generator 520, the example discrepancy resolver 522,the example blob tally generator 524, the example active regionidentifier 600, the example frame segmenter 604, the example fluctuationcalculator 606, the example threshold calculator 610, the examplecomparator 612, the example segment linker 614, the example facedetection logic 602, the example fluctuation-based eliminator 700, theexample correlation-based eliminator 702, the example false positiveidentifier 704, the example checker 706, the example pixel intensitycalculator 708, the example fluctuation calculator 710, the exampleaverager 714, the example comparator 716, the example locationcalculator 718, the example prior frame retriever 720, the example falsepositive verifier 722, the example location calculator 724, the exampleprior frame retriever 726, the example data extractor 728, the examplescore generator 730, the example comparator 732, the example overlapregion analyzer 800, the example data extractor 802, the example scoregenerator 804, the example comparator 806, the example correlated facehistory 808, the example location calculator 810, the example searcher812, the example analyzer 814, the example location calculator 900, theexample comparator 902, the example combiner 904, the example overlapregion analyzer 1000, the example data extractor 1002, the example scoregenerator 1004, the example comparator 1006, the example pixel whitener1100, the example blob creator 1102, the example center of gravitycalculator 1104, the example location analyzer 1106, the example adder1108, and/or, more generally, the example people counter 406 of FIGS.5-11 could be implemented by one or more circuit(s), programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)), etc. When any of the appended apparatus or systemclaims of this patent are read to cover a purely software and/orfirmware implementation, at least one of the example interval tracker500, the example face detector 502, the example face data tracker 504,the example static false positive eliminator 512, the example frame pairoverlap eliminator 514, the example grouper 516, the example groupoverlap eliminator 518, the example group tally generator 520, theexample discrepancy resolver 522, the example blob tally generator 524,the example active region identifier 600, the example frame segmenter604, the example fluctuation calculator 606, the example thresholdcalculator 610, the example comparator 612, the example segment linker614, the example face detection logic 602, the example fluctuation-basedeliminator 700, the example correlation-based eliminator 702, theexample false positive identifier 704, the example checker 706, theexample pixel intensity calculator 708, the example fluctuationcalculator 710, the example averager 714, the example comparator 716,the example location calculator 718, the example prior frame retriever720, the example false positive verifier 722, the example locationcalculator 724, the example prior frame retriever 726, the example dataextractor 728, the example score generator 730, the example comparator732, the example overlap region analyzer 800, the example data extractor802, the example score generator 804, the example comparator 806, theexample correlated face history 808, the example location calculator810, the example searcher 812, the example analyzer 814, the examplelocation calculator 900, the example comparator 902, the examplecombiner 904, the example overlap region analyzer 1000, the example dataextractor 1002, the example score generator 1004, the example comparator1006, the example pixel whitener 1100, the example blob creator 1102,the example center of gravity calculator 1104, the example locationanalyzer 1106, the example adder 1108, and/or, more generally, theexample people counter 406 of FIGS. 5-11 are hereby expressly defined toinclude a tangible computer readable medium such as a memory, DVD, CD,Blu-ray, etc. storing the software and/or firmware. Further still, theexample people counter 406 of FIGS. 5-11 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 5-11, and/or may include more than one of any orall of the illustrated elements, processes and devices.

FIGS. 12-19 are flowcharts representative of example machine readableinstructions for implementing the example people counter 406 of FIGS.4-11. In the example flowcharts of FIGS. 12-19, the machine readableinstructions comprise program(s) for execution by a processor such asthe processor 2012 shown in the example computer 2000 discussed below inconnection with FIG. 20. The program(s) may be embodied in softwarestored on a tangible computer readable medium such as a CD-ROM, a floppydisk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or amemory associated with the processor 2012, but the entire program and/orparts thereof could alternatively be executed by a device other than theprocessor 2012 and/or embodied in firmware or dedicated hardware.Further, although the example program(s) is described with reference tothe flowcharts illustrated in FIGS. 12-19, many other methods ofimplementing the example people counter 406 may alternatively be used.For example, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 12-19 may beimplemented using coded instructions (e.g., computer readableinstructions) stored on a tangible computer readable medium such as ahard disk drive, a flash memory, a read-only memory (ROM), a compactdisk (CD), a digital versatile disk (DVD), a cache, a random-accessmemory (RAM) and/or any other storage media in which information isstored for any duration (e.g., for extended time periods, permanently,brief instances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term tangible computer readable mediumis expressly defined to include any type of computer readable storageand to exclude propagating signals. Additionally or alternatively, theexample processes of FIGS. 12-19 may be implemented using codedinstructions (e.g., computer readable instructions) stored on anon-transitory computer readable medium such as a hard disk drive, aflash memory, a read-only memory, a compact disk, a digital versatiledisk, a cache, a random-access memory and/or any other storage media inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, brief instances, for temporarily buffering, and/orfor caching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. Thus, a claim using “at least” as thetransition term in its preamble may include elements in addition tothose expressly recited in the claim.

The example of FIG. 12 begins with an initialization of the examplepeople counter 406 of FIGS. 4 and/or 5-11 (block 1200). The intervaltracker 500 (FIG. 5) is initialized to zero (block 1202) to begintracking a period time for which the example people counter 406 is togenerate a people tally representative of an amount of people located inthe example media exposure environment 100 of FIG. 1. The first andsecond image sensors 404 and 405 provide the people counter 406 with aframe pair, which includes image data of the media exposure environment100 (block 1204).

The example face detector 502 executes a face detection operation thatanalyzes the image data to search for human faces (block 1206). Anexample implementation of block 1206 is shown in FIG. 13. The example ofFIG. 13 begins with the frame segmenter 604 of the active regionidentifier 600 dividing a first frame of the frame pair (e.g., the framecaptured by the first image sensor 404) into a plurality of segments(e.g., 50×50 pixel squares) (block 1300). The example fluctuationcalculator 606 determines pixel intensities (or other image datacharacteristic(s)) of the segmented image and determines an average RMSfor each segment (block 1302). As described above, the RMS for eachsegment is calculated based on the running RMS values for each pixeland/or segment in the fluctuation database 608. The RMS value for eachsegment with the current first frame incorporated therein is stored inthe fluctuation database 608. The threshold calculator 610 calculates anaverage RMS for the current frame using a randomly selected set of pixellocations (block 1304). The average RMS is used as the threshold for thecurrent frame. In particular, the comparator 612 compares the RMS valueof each segment to the threshold (block 1306). The example comparator612 designates segments having an RMS value greater than the thresholdas active segments (block 1308). The segment linker 614 link thesegments designated as active together to form one or more regions ofinterest (block 1310). The example active region identifier 600identifies regions of interest for both frames of the current framepair. Thus, if both frames have not been analyzed (block 1312), controlreturns to block 1300. Otherwise, the face detection logic 602 detectsfaces in the region(s) of interest formed at block 1310 (block 1314).Control returns to block 1208 of FIG. 12 (block 1316). When the facedetector 502 detects a face, the face detector 502 also creates a facerectangle 300 (or any suitable shape) surrounding the detected face of aperson 302. The face rectangle(s) generated by the example face detector502 in connection with the first image sensor 404 are stored in thefirst set of face rectangles 506 and the face rectangle(s) generated bythe example face detector 502 in connection with the second image sensor405 are stored in the second set of face rectangles 508.

The example static false positive eliminator 512 removes false positivesfrom the sets of face rectangles 506 and 508 (block 1208). An exampleimplementation of block 1208 is shown in FIGS. 14A and 14B. The exampleof FIG. 14A corresponds to the example fluctuation-based eliminator 700of FIG. 7. As described above, the example fluctuation-based eliminator700 of FIG. 7 includes a false positive identifier 704 and a checker706. In the example of FIG. 14A, the pixel intensity calculator 708 ofthe false positive identifier 704 calculates an intensity value of eachpixel of a current frame of image data (block 1400). The calculatedintensity values are incorporated into respective RMS values calculatedby the example fluctuation calculator 710 (block 1402). The average 714averages RMS values of a random set of pixels for the current frame(block 1404). The example averager 714 also calculates an average RMSvalue for the pixels of each detected face rectangle for the currentframe (block 1406). The example comparator 716 compares the average RMSvalue of the random set of pixels across the current frame to theaverage RMS of the respective face rectangle (block 1408). If a facerectangle has an average RMS value less than or equal to the average RMSvalue of the random set of pixels, that face rectangle is designated asa false positive (block 1410). As described above, the example checker706 may supplement the detections of the false positive identifier 704.In the example of FIG. 14A, if the services of the checker 706 aredesired or set to be executed (block 1412), control proceeds to FIG.14B. Otherwise, the identified false positives are eliminated from thesets of face rectangles 506 and 508 (block 1414) and control returns toblock 1210 of FIG. 12 (block 1416).

In the example of FIG. 14B, the example checker receives the facerectangles designated as false positives by the false positiveidentifier 704 (block 1418). The example location calculator 718 of theexample checker 706 determines coordinates at which a received falsepositive face rectangle is located (block 1420). The prior frameretriever 720 retrieves data from the frame database 510 (FIG. 5) usingthe calculated location (block 1422). As described above, the exampleframe database 510 includes historical data indicative of successfulface detections and the corresponding coordinates. If, according to thedatabase 510, a face was not detected and/or verified as present at thecalculated location in at that calculated location in previous frame(s)(block 1424), the false positive verifier 722 verifies that the receivedface rectangle is a false positive (block 1426). The previous frame(s)to be queried may be, for examples, frames captured within a thresholdperiod of time previous to the current frame (e.g., the twelve priorframes). Otherwise, if a face was detected and/or verified at thecalculated location in the previous frame(s) (block 1424), the falsepositive verifier 722 disqualifies the corresponding face rectangle as afalse positive and, instead, marks the face rectangle as a true positive(block 1428). Control returns to block 1420 if all face rectangles fromdata received from the false positive identifier 704 have not beenanalyzed (block 1430). Otherwise, control returns to block 1210 of FIG.12 (block 1432).

In addition to or in lieu of the examples described in FIGS. 14A and14B, the example correlation-based eliminator 702 of FIG. 7 alsoeliminates false positives from the frame data. Another exampleimplementation of block 1208 of FIG. 12 corresponding to the examplecorrelation-based eliminator 702 is shown in FIG. 15. As describedabove, the example correlation-based eliminator 702 uses a comparisonbetween a current frame and historical frame data to determine whetherone or more of the face rectangles of the sets of face rectangles 506and/or 508 are static false positives. The location calculator 724 ofthe example correlation-based eliminator 702 of FIG. 7 determines thecoordinates of a detected face rectangle by, for example, accessing thelocation information stored in connection with the current frame in theface data tracker 504 (FIG. 5) (block 1500). The example prior frameretriever 726 retrieves a previous frame from the frame database 510(block 1502). The example data extractor 728 of FIG. 7 extracts imagedata from the current frame and the retrieved previous frame at thecoordinates provided by the location calculator 724 corresponding to theface rectangle (block 1504). The score generator 730 generates acorrelation score representative of a similarity between the image dataextracted from the received face rectangle and the image data extractedfrom the previous frame at the calculated location of the received facerectangle (block 1506). The comparator 732 determines whether therespective correlation score exceeds a threshold (block 1508). If so,the corresponding face rectangle is designated as a false positive andthe corresponding set of face rectangles 506 or 508 is updated (block1510). Otherwise, the example correlation-based eliminator 702determines whether each face rectangle has been analyzed (block 1512).If not, control returns to block 1500. Otherwise, control returns toblock 1210 of FIG. 12 (block 1514).

Referring to FIG. 12, the example frame pair overlap eliminator 514(FIG. 5) eliminates overlap from the current frame pair. An exampleimplementation of block 1210 is illustrated in FIG. 16. In the exampleof FIG. 16, the overlap region analyzer 800 analyzes the overlap region204 (FIG. 2) and identifies a face rectangle detected by the secondimage sensor 404 in the overlap region 204 (block 1600). Using the dataextractor 802, the score generator 804, and the comparator 806 of theexample frame pair overlap eliminator 514, the face rectangle detectedin the overlap region 204 in connection with the second image sensor 405is compared to the face rectangles detected in connection with the firstimage sensor 204 in the current frame pair (block 1602). If the facerectangle detected in the overlap region 204 is similar within athreshold to any of the face rectangles detected in connection with thefirst image sensor 404 (block 1604), the location of the face rectangledetected in the overlap region 204 (as detected by the overlap regionanalyzer 800) and the location of the matching face rectangle detectedin connection with the first image sensor 404 are calculated (block1606). Further, an entry is added to the correlated face history 808including the calculated locations (block 1608).

Referring to block 1604, if the face rectangle detected in the overlapregion 204 in connection with the second image sensor 405 is not similarwithin the threshold to any face rectangle detected in connection withthe first image sensor 404, the searcher 812 queries the correlated facehistory 808 (block 1610). In particular, the searcher 812 determineswhether the history 808 includes an entry having second-cameracoordinates corresponding to the coordinates of the face rectangledetected in the overlap region 204 in connection with the second imagesensor 405. If so, the corresponding first-camera coordinates of thematching history entry is determined (block 1612). If a face rectangleis detected in connection with the first image sensor 404 the currentframe pair having a location corresponding to the first-cameracoordinates obtained from the history 808 (block 1614), the analyzer 814determines that the face rectangle detected in connection with thesecond image sensor 405 in the overlap region 204 in the current frameis redundant (block 1616). That is, the analyzer 814 determines that thetwo face rectangles of the current frame pair have the same locations asa pair of face rectangles previously determined to be redundant and,thus, are also redundant. Face rectangles determined to be redundant bythe example comparator 806 or the example analyzer 814 are eliminatedfrom the second set of face rectangles 508 so that those face rectanglesare not double counted in later analyses (block 1616). Control returnsto block 1212 of FIG. 12 (block 1618).

Referring to FIG. 12, blocks 1208 and 120 have conditioned the data ofthe current frame pair loaded at block 1204 to remove static falsepositives and overlapping face rectangles. The interval tracker 500increments the counter (block 1212). If the counter has not yet reacheda threshold or trigger (block 1214), control returns to block 1204 andanother frame pair is loaded. Otherwise, the example grouper 516 istriggered to execute on the face conditioned face rectangles of the sets506 and 508 (block 1216). An example implementation of block 1216 isillustrated in FIG. 17. In the example of FIG. 17, the locationcalculator 900 of the example grouper 516 calculates or obtains thelocation of the face rectangles of the face data tracker 504 (e.g., theface rectangle sets 506 and 508), which includes the face rectanglessurviving the elimination or conditioning processes of blocks 1208 and1210 (block 1700). The example comparator 902 of the grouper 516compares the calculated or obtained locations of the respective facerectangles (block 1702). In particular, the example comparator 902determines whether any of the surviving face rectangles 506 collectedover the defined time interval in connection with the first image sensor404 have coordinates that are similar within a threshold. Further, theexample comparator 902 determines whether any of the surviving facerectangles 508 collected over the defined time interval in connectionwith the second image sensor 405 have coordinates that are similarwithin a threshold. For each of the first and second sets of facerectangles 506 and 508, the combiner 904 groups similarly located facerectangles together to form a group (block 1704). Further, the combiner904 eliminates any of the formed groups that have less than a thresholdamount of members to eliminate, for example, transient face detections(block 1706). Control returns to block 1220 of FIG. 12 (block 1708).

Referring to FIG. 12, the group overlap eliminator 518 eliminatesoverlap among the groups formed by the example grouper 516 (block 1218).An example implementation of block 1218 is illustrated in FIG. 18. Inthe example of FIG. 18, the overlap region analyzer 1000 of the examplegroup overlap eliminator 518 identifies one of the group(s) formed bythe grouper 516 in connection with the second image sensor 405 (block1800). Image data of the face rectangles of the identified group arecompared to image data of the face rectangles detected in connectionwith the first image sensor 404 (block 1802). For each of thecomparisons, the score generator 1004 generates a correlation score(block 1804). If the group identified in block 1800 includes a facerectangle having a correlation score with the image data of the firstimage sensor 404 greater than a threshold according to the comparator1006 (block 1806), the identified group is designated as a redundantgroup (block 1808). Further, the overlap group is eliminated from beingcounted in a people tally for the current time interval (block 1810).Control returns to block 1220 of FIG. 12 (block 1812).

Referring to FIG. 12, the group overlap eliminator 518 provides thesurviving groups to the group tally generator 520, which counts thenumber of surviving groups to form a people tally for the period of timedefined by the interval tracker 500 (block 1220). In the illustratedexample of FIG. 12, the example blob tally generator 524 also generatesa people count for the period of time defined by the interval tracker500 (block 1222). An example implementation of block 1222 is illustratedin FIG. 19. To generate the blob count for the defined period of time,the pixel whitener 1100 of the example blob tally generator 524 of FIG.11 whitens each pixel of the detected face rectangles 506 and 508 of theface data tracker 504 (that survived the conditioning of blocks 1208 and1210). Additionally, the example pixel whitener 1100 blackens each pixelnot corresponding to a detected face. The blob creator 1102 uses thewhitened/blackened pixel data to identify individual blobs that likelycorrespond to a person (block 1902). For example, the blob creator 1102combines or overlays the whitened/blackened image data spanning acrossthe period of time and combines the whitened pixels of the overlaidimage to form a blob. To avoid double counting of redundant blobs, thecenter of gravity calculator 1104 calculates a center of the createdblobs associated with the second set of face rectangles 508 and, thus,the second image sensor 405 (block 1904). The location analyzer 1106determines whether the calculated center for a blob associated with thesecond image sensor 405 falls within boundaries of the overlap region204 (block 1906). If so, the corresponding blob is eliminated from theblob image formed by the blob creator 1102 (block 1908). Otherwise, orafter the redundant blobs are eliminated, the adder 1108 adds thesurviving blobs together to form a blob people tally (block 1910).Control returns to block 1224 of FIG. 12 (block 1912).

Referring to FIG. 12, the group count generated at block 1220 and theblob count generated at block 1222 are reported to the discrepancyresolver 522, which resolves any discrepancy between the two counts(block 1224). As described above, if a discrepancy is found between thegroup count and the blob count for the period of time defined by theinterval tracker 500, the discrepancy resolver 522 analyzes entries ofthe tally database 526 corresponding to previous periods of timeproximate the current time interval. In the example of FIG. 12, theexample discrepancy resolver 522 uses the data associated with precedingperiods of time to choose one of the differing people counts. Forexample, the discrepancy resolver 522 chooses whichever of the groupcount and the blob count is closest (e.g., matches) an average peopletally of the preceding time periods of the database 526. The peoplecount selected by the discrepancy resolver 522 (or the common peoplecount when no discrepancy exists between the group count and the blobcount) is stored in the tally database 526. The counter of the intervaltracker 500 is reset and control returns to block 1204 (block 1226).

While the example people counter 406 of FIGS. 2 and/or 3 is described inthe context of an audience measurement device 104 and the generation ofexposure data for media, the example methods, articles of manufacture,and apparatus disclosed herein can be applied to additional oralternative contexts, systems, measurements, applications, programs,etc. That is, the example methods, articles of manufacture, andapparatus disclosed herein can be used in any application to determinehow many people are located in a space or location.

FIG. 20 is a block diagram of a processor platform 2000 capable ofexecuting the instructions of FIGS. 12-19 to implement the peoplecounter 406 of FIGS. 4-11. The processor platform 2000 can be, forexample, a server, a personal computer, an Internet appliance, a DVDplayer, a CD player, a digital video recorder, a Blu-ray player, agaming console, a personal video recorder, a set top box, or any othertype of computing device.

The processor platform 2000 of the instant example includes a processor2012. For example, the processor 2012 can be implemented by one or moremicroprocessors or controllers from any desired family or manufacturer.

The processor 2012 includes a local memory 2013 (e.g., a cache) and isin communication with a main memory including a volatile memory 2014 anda non-volatile memory 2016 via a bus 2018. The volatile memory 2014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 2016 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 2014,2016 is controlled by a memory controller.

The processor platform 2000 also includes an interface circuit 2020. Theinterface circuit 2020 may be implemented by any type of interfacestandard, such as an Ethernet interface, a universal serial bus (USB),and/or a PCI express interface.

One or more input devices 2022 are connected to the interface circuit2020. The input device(s) 2022 permit a user to enter data and commandsinto the processor 2012. The input device(s) can be implemented by, forexample, a keyboard, a mouse, a touchscreen, a track-pad, a trackball,isopoint and/or a voice recognition system.

One or more output devices 2024 are also connected to the interfacecircuit 2020. The output devices 2024 can be implemented, for example,by display devices (e.g., a liquid crystal display, a cathode ray tubedisplay (CRT), a printer and/or speakers). The interface circuit 2020,thus, typically includes a graphics driver card.

The interface circuit 2020 also includes a communication device such asa modem or network interface card to facilitate exchange of data withexternal computers via a network 2026 (e.g., an Ethernet connection, adigital subscriber line (DSL), a telephone line, coaxial cable, acellular telephone system, etc.).

The processor platform 2000 also includes one or more mass storagedevices 2028 for storing software and data. Examples of such massstorage devices 2028 include floppy disk drives, hard drive disks,compact disk drives and digital versatile disk (DVD) drives. The massstorage device 2028 may implement the frame database 510 and/or thefluctuation database 712.

The coded instructions 2032 of FIGS. 12-19 may be stored in the massstorage device 2028, in the volatile memory 2014, in the non-volatilememory 2016, and/or on a removable storage medium such as a CD or DVD.

In some example implementation, the example people counter 406 of FIGS.4-111 is implemented in connection with an XBOX® gaming system. In someexamples, the one or more sensors associated with the example peoplecounter 406 of FIGS. 4-11 are implemented with KINECT® sensors (e.g., tocapture images of an environment to count people). In some examples,some or all of the machine readable instructions of FIGS. 12-19 can bedownloaded (e.g., via the Internet) to and stored on an XBOX® gamingconsole that implementing the example people counter 406 of FIGS. 4-11.

Although certain example apparatus, methods, and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all apparatus,methods, and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method, comprising: dividing a first frame ofimage data into a plurality of segments; calculating, with a processor,first fluctuation factors for respective ones of the segments, the firstfluctuation factors respectively representative of changes in therespective segments from a second frame of image data to the first frameof image data; calculating, with the processor, a second fluctuationfactor for the first frame; before performing object detection on thefirst frame: identifying ones of the plurality of segments of the firstframe having a first fluctuation factor greater than the secondfluctuation factor as active segments for the first frame; and linking,with the processor, adjacent ones of the active segments of the firstframe to form a region of interest in the first frame; and performing,with the processor, the object detection on the region of interest.
 2. Amethod as defined in claim 1, wherein the adjacent ones of the activesegments are adjacent within a margin of error.
 3. A method as definedin claim 1, further comprising not performing the object detection onareas of the first frame outside the region of interest.
 4. A method asdefined in claim 1, wherein the linking of the adjacent ones of theactive segments comprises forming a shape that includes the adjacentones of the active segments.
 5. A method as defined in claim 1, whereinthe second fluctuation factor is calculated by selecting a set of pixelsfrom the image data of the first frame and averaging fluctuationcharacteristics of the selected pixels.
 6. A method as defined in claim1, wherein the first and second fluctuation factors are root mean squarevalues based on pixel intensities.
 7. A method as defined in claim 1,wherein the second fluctuation factor is representative of a collectivechange across multiple ones of the segments over time.
 8. A tangiblecomputer readable storage medium comprising instructions that, whenexecuted, cause a machine to at least: divide a first frame of imagedata into a plurality of segments; calculate first fluctuation factorsfor respective ones of the segments, the first fluctuation factorsrespectively indicative of fluctuations of the respective segments froma second frame of image data to the first frame; calculate a secondfluctuation factor for the first frame; before performing objectdetection on the first frame: identify ones of the plurality of segmentsof the first frame having a first fluctuation factor greater than thesecond fluctuation factor as active segments for the first frame; andlink a first one of the active segments of the first frame and a secondone of the active segments of the first frame adjacent the first activesegment to form a region of interest in the first frame; and perform theobject detection on the region of interest.
 9. A tangible computerreadable storage medium as defined in claim 8, wherein the first andsecond ones of the active segments are adjacent within a margin oferror.
 10. A tangible computer readable storage medium as defined inclaim 8, wherein the instructions, when executed, cause the machine toperform the object detection on the region of interest and not performthe object detection on areas of the first frame not in the region ofinterest.
 11. A tangible computer readable storage medium as defined inclaim 8, wherein the linking of the first and second ones of the activesegments comprises forming a shape that includes the first and secondones of the active segments.
 12. A tangible computer readable storagemedium as defined in claim 8, wherein the instructions, when executed,cause the machine to calculate the second fluctuation factor byselecting a set of pixels from the image data of the first frame andaveraging fluctuation characteristics of the selected pixels.
 13. Atangible computer readable storage medium as defined in claim 8, whereinthe second fluctuation factor is representative of a collective changeacross multiple ones of the segments over time.
 14. An apparatus,comprising: a segmenter to divide first and second frames of image datainto a plurality of segments; a fluctuation calculator to respectivelydetermine a first fluctuation factor for respective ones of thesegments, the first fluctuation factors representing changes across thefirst frame and the second frame, the first and second frames capturedat different times, the fluctuation calculator to determine a collectivefluctuation factor for the first frame, the collective fluctuationfactor indicative of a collective fluctuation of the first frame acrossthe different times; a comparator to, before performance of objectdetection on the first frame, mark ones of the segments of the firstframe having a corresponding one of the first fluctuation factorsgreater than the collective fluctuation factor as active segments forthe first frame; and a linker to, before performance of the objectdetection on the first frame, combine adjacent ones of the activesegments of the first frame to form a region of interest of the firstframe; and an object detector to perform the object detection on theregion of interest of the first frame.
 15. An apparatus as defined inclaim 14, wherein the adjacent ones of the active segments are adjacentwithin a margin of error.
 16. An apparatus as defined in claim 14,wherein the object detector comprises face detection logic, the facedetection logic to operate on the region of interest but not on portionsof the first frame outside the region of interest.
 17. An apparatus asdefined in claim 14, wherein the linker is to link the adjacent ones ofthe active segments by forming a shape that includes the adjacent onesof the segments.
 18. An apparatus as defined in claim 14, wherein thefluctuation calculator is to calculate the collective fluctuation factorby selecting a random set of pixels from the image data of the firstframe and averaging fluctuation characteristics of the randomly selectedset of pixels.
 19. An apparatus as defined in claim 14, wherein thefirst fluctuation factors and the collective fluctuation factor are rootmean square values based on pixel intensities.